@inproceedings{a5c3dacf2450463681f160aa6ec875ab,
title = "Opinion filtered recommendation trust model in peer-to-peer networks",
abstract = "A multiagent distributed system consists of a network of heterogeneous peers of different trust evaluation standards. A major concern is how to form a requester's own trust opinion of an unknown party from multiple recommendations, and how to detect deceptions since recommenders may exaggerate their ratings. This paper presents a novel application of neural networks in deriving personalized trust opinion from heterogeneous recommendations. The experimental results showed that a three-layered neural network converges at an average of 12528 iterations and 93.75% of the estimation errors are less than 5%. More important, the model is adaptive to trust behavior changes and has robust performance when there is high estimation accuracy requirement or when there are deceptive recommendations.",
author = "Weihua Song and Phoha, {Vir V.}",
year = "2005",
doi = "10.1007/11574781_23",
language = "English (US)",
isbn = "3540297553",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "237--244",
booktitle = "Agents and Peer-to-Peer Computing - Third International Workshop, AP2PC 2004, Revised and Invited Papers",
note = "Third International Workshop on Agents and Peer-to-Peer Computing, AP2PC 2004 ; Conference date: 19-07-2004 Through 19-07-2004",
}